Intelligent Failure Detection in Underwater Sensor Networks

Beneath the ocean's depths, Underwater Wireless Sensor Networks (UWSNs) silently monitor critical environmental changes, yet they battle relentless adversaries corrosion, biofouling, and energy depletion. High salinity accelerates wear, fluctuating temperatures weaken components, and immense pressure threatens structural integrity. Acoustic communication, the backbone of underwater data transfer, struggles with high latency and signal disruptions, making real-time monitoring a challenge. As batteries deplete and sensors drift, the network?s reliability hangs in the balance. To combat these challenges, this study introduces a failure classification framework based on three key indicators like corrosion level, battery status, and packet loss rate. A node fails when corrosion exceeds critical limits, energy reserves drop below operational thresholds, or communication losses become unsustainable. Machine learning models trained on simulated and real-world data predict failures before they occur, allowing proactive intervention. Advanced data preprocessing techniques enhance predictive accuracy, ensuring robust network longevity. By integrating intelligent monitoring and predictive maintenance, this research paves the way for resilient UWSNs, safeguarding long-term underwater sensing in the face of nature?s relentless forces.